Psychological Research Methods

Psychological Research Methods

Journal What are the ethical limitations of psychological experimentation?

Vocabulary Placebo Effect Hypothesis Informed Consent
Reliability Validity Random Selection Random Assignment Standardization Descriptive Statistics Inferential Statistics Measures of Central Tendency (mean, median, mode) know how to calculate Variability Standard Deviation (know how to calculate) Correlation Coefficient R (know what the graphs look like) Replication Double-Blind Procedure Hypothesis Operational Definition Participants/Subjects Within-subjects Design Between-subjects Design Independent Variable Dependent Variable Correlation Case Study Naturalistic Observation Archival Research Survey Experimental Group Control Group Confounding Variable

Scientific Method Observation/Question Literature Review Hypothesis
Study design Statistical analysis Report findings Consider Limitations and ideas for future research

Hypothesis: a tentative and testable statement about the relationship between variables The effect of ________ on _________ Independent Variable: variable that is intentionally manipulated by the experimenter. Dependent Variable: variable that is measured – the outcome variable. The variables must have clear operational definitions Confounding Variables: other factors that may influence the dependent variable other than the independent variable. These extraneous factors are not an intentional part of the experiment. Confounding variables are NOT COOL

Types of Research Correlational Experimental Quasi-Experimental
Naturalistic observation Surveys Case Studies Archival Analysis Experimental Quasi-Experimental

Correlational The variables are only measured/observed Participants are not randomly assigned to conditions Some relationships can only be investigated using correlational designs due to practicality or ethical concerns Smoking & Cancer in humans Cannot imply causality Vulnerable to confounding variables Includes Naturalistic Observation, Survey, and Case Studies, Archival research

Correlation is not Causation

Naturalistic Observation Observation and data collection in it’s natural setting without direct intervention Good place to start when first investigating a phenomenon Great ecological validity: the extent to which findings represent what happens in the real world People may change their behavior if they think they are being watched

Naturalistic Observation

Surveys The use of questionnaires or interviews to gather information of participants attitude, behavior, and beliefs Data collection is relatively quick, easy, and inexpensive Self-report data can be unreliable and inaccurate Social Desirability Forgetfulness

Case Studies The in-depth analysis of a single individual using direct interviews, observations, and other personal records Frequently used for the study of psychological disorders or rare neurological conditions Provides a detailed, holistic narrative of the many factors that contribute to an individual’s current state Clinical samples are often unrepresentative and suffer from sampling bias (“cherry picking”)

Archival Analysis Involves seeking out, compiling, and analyzing various forms of evidence from original archival records U.S. Census data Arrest records Evidence can only be collected in hindsight Constrained by the records’ availability and accuracy

Experimental CAN imply causal relationships! The independent variable is manipulated in the experiment; the dependent variable is measured after the manipulation Independent = cause Dependent = effect Subjects/Participants are randomly assigned to conditions Experimental Group (receives manipulation) Control Group (no manipulation)

Random Sampling Having access to the entire population and randomly picking individuals from that population Allows for generalization of results to the entire population If the sample is large enough, it should be representative of the entire population Random Assignment Randomly assigning the sample of subjects into experimental/control conditions Mitigates threats to validity – gets rid of confounding variables

Quasi-Experimental Has everything else found in a true experiment, except Random Assignment. A treatment or intervention is given to some people but not others In some cases, random assignment may be unethical or impractical Quasi-Experimental designs are vulnerable to confounding variables Participants are placed into groups by some other selection criteria Case severity Family environments

Operational Definitions improve reliability and validity by providing clear, concrete descriptions of the variables Important for many psychological constructs that are vague or multifaceted Intelligence Aggression BAD Definition “Aggression will be measured by how mean the participant is to others” BETTER Definition “Aggression will be measured by how many times the participant hits other children in a 30 minute period on the playground”

Between-Subjects design Separate groups of people are assigned different treatments Within-Subjects Design The same people experience the different treatments and are re-tested Before/After Matched-Pairs Design Pairs of subjects (or parts of subjects) are separated and given different treatments

Quantitative variables Can be measured using numerical values Lends itself easily to statistical analysis Qualitative Variables Descriptive data that cannot be easily converted into a numerical score Includes type, kind, category, etc. Typically more in-depth Quantitative  Qualitative 

Causal Relationships: Causal relationships can only be determined in controlled experiments and must meet 3 requirements: The cause (independent variable) must precede the effect (dependent variable) in time The cause and effect covarry (they have a predictable relationship) The relationship between the variables cannot be explained by another third or confounding variable

The Six Kinds of Causal Relationships Unanalyzed (correlational) Direct Indirect (Mediated) Bidirectional Moderated Spurious

Unanalyzed (Correlation): a predicable relationship exists between the two variables, however an experiment has not been conducted to determine how exactly they are related X Y

Direct: After an experiment has been conducted, the given variable (x) is direct cause of another variable (y). It is known which precedes the other in time, they have a predictable relationship, and no other variable (confound) could possibly be responsible. X Y

Indirect (Mediated): The variable x does have an effect on the variable y, but only through it’s impact on a mediator, z. This is a domino or cascading causal effect. X Z Y

Bidirectional: Given variable (x) has causal influence on variable (y) which in turn has causal impact on (x). X Y

Moderated: Interaction effects in which relationship between variable (x) (focal independent variable) and variable (y) is altered by a third variable (z) (moderator variable). X Y Z

Spurious: variable (x) and variable (y) are related but only because of a common cause, variable (z). Z X Y

Reliability The consistency of data resulting from psychological testing or experimental research Validity The information produced by the research accurately reflects the variables that were intended to manipulate or measure Standardization a set of uniform procedures for treating each participant in a test, interview, or experiment. Standard Operating Procedures (SOP’s) should be followed to ensure each participant/client is treated the same If standardization is not followed, confounding variables may influence and invalidate the results.

Measures of Reliability: Test re-test Inter-rater Split-half Measures of Validity: Face Validity Predictive Validity Concurrent Validity

Threats to Validity: Observer bias Keep observers “blind” from the hypothesis Self-fulfilling prophecy Distance the Principal Investigator from experiment Participants should not know the hypothesis Placebo/Expectancy effects Use control groups Confounding variables Randomly assign subjects to groups With a large enough sample, any individual weirdness of subjects should be evenly distributed between the groups

Blind & Double Blind Procedures Participants are usually “blind” to the true hypothesis of the experiment and to what condition they are in Only told the true nature of the experiment after it is completed In Double Blind Procedures, both the participant and the experimenter do not know what condition the participant is in This minimizes the effect of both participant and experimenter bias

Standardization is key to conducting a valid experiment A researcher must treat each participant/subject exactly the same except for the intended manipulation Practicality must be addressed while still maintaining scientific rigor

#overlyhonestmethods

Longitudinal Designs Follows the same individuals over time Observes developmental or treatment trajectory Can take considerable time and expense to conduct Issues with internal validity History Maturation Attrition Cross-Sectional Designs Samples different individuals from various time points Cannot imply a trajectory Less costly and time consuming than Longitudinal Designs Prone to cohort effects

Ethics in Psychological Research

Ethical Research with Living Things
Humans = Participants Animals = Subjects Human research is governed at the university level by the Institutional Review Board (IRB) Animal research is governed at the university level by the Institutional Animal Care and Use Committee (IACUC) and also at the federal level by the Food and Drug Administration and the U.S. Department of Agriculture, amongst other entities

Principles of Research with Human Participants
“Psychologists strive to benefit those with whom they work and take care to do no harm…Psychologists respect the dignity and worth of all people, and the rights of individuals to privacy, confidentiality, and self- determination.” -Ethical Principles of Psychologists and Code of Conduct, APA

Risk/Benefit analysis: comparison of the potential benefit (both to the individual and the population) and the risk of harm to the participant Only if the benefit outweighs the risk can a study be considered ethical The allowable risk associated for a study investigating a cure for cancer is higher than a study looking at productivity in the workplace If more than ‘minimal risk’ is involved in a study, increased scrutiny is placed on the investigator to justify their method

Informed consent: the participant must be aware of what they will be doing, what risk may be involved, and their rights as a participant. In order to give consent, the participant must be 18 years or older and be able to give consent. Informed Assent is given to minors or special populations, along with the consent of their legal guardian Some level of deception is required in most studies; the participant must be “blind” to the specific hypothesis to avoid threats to validity.

Rights of Human Participants Freedom to withdrawal at any time Without penalty Confidentiality All participant information must be kept confidential Exceptions: Harm to self, Harm to others, Neglect, Potential victimization “Baker Act” Debriefing and protection from harm Since most experiments require from deception at the beginning, participants must be told the true purpose of the study after they have completed the experiment

Animal Research?

Animal Research.

Principles of Research with Animal Subjects
Animal research carried out with the intention of generalizing results to humans is typically called comparative research Using an animal model, the goal is to investigate comparable behaviors or physiology with humans Some experiments would be unethical or impractical to be conducted on humans With prior animal trails, treatments can then enter human trials with less risk

Approved methods and ethical standards differ depending on the species and country Research with great apes is banned in several countries (European Union) and severely limited in the U.S. “Higher” functioning or social species (i.e. other non-human primates) tend to have more strict guidelines for experimentation compared to “less sophisticated” animals Mammals tend to have more strict guidelines than other classes of animals

Risk/Benefit analysis is also carried out with animal subjects – but researchers aren’t as concerned about the benefit to the individual animal Ethical research with animals strives to: Reduce: use as few animals as possible to get the desired information Replace: replace sentient animal models with alternatives, such as tissue analysis and computer models Refine: the use of methods to minimize pain, suffering, and distress. Also includes the use of enrichment to enhance animal welfare

Issues arise with the degree to which information from animal models can be generalized to humans. Basic biological and behavioral phenomenon should be similar across species Neuroscience Behaviorism Many studies could ONLY be ethically conducted with nonhuman animals Intervention may be too risky with humans Can produce data quicker and on a larger scale Some studies require the sacrifice of the organism to study structural brain changes

Group Activity Think of a research question that you could use both human and animal methodology to investigate Create two separate designs to investigate this hypothesis, one with humans and one with animals Consider using different kinds of designs (correlational, experimental) with each species – consider practical and ethical limitations

Introduction to Statistics

Statistics Supplement
Descriptive Statistics: used to describe different aspects of numerical data; used only to describe the sample. Includes measures of central tendency, variability, and correlation Inferential Statistics: uses probability theory to allow researchers to generalize and predict results of the population outside of the sample; used to determine whether or not a hypothesis is supported or rejected Can make inferences about a population from a sample Can compare multiple groups to each other (i.e. experimental and control)

Measures of central tendency Mean Median Mode Variability Range Standard Deviation

Normal Distribution – the “bell curve”

Many things are normally distributed: Intelligence Scores [(Mental Age) / (Chronological Age)] x 100 = IQ Mean IQ = 100 Std. dev = 15 IQ over 130 is exceptionally smart IQ under 70 is one criteria for Intellectual Disability

We use assumptions about the normal distribution in order to determine if data collected comply with a known distribution or are significantly different You can also compare a single score to a population distribution to see how rare it is Percentiles and standardized tests

SAT v. ACT SAT Mean = 1026 SD = 209 ACT Mean = 20.8 SD = 4.8 Which is better, getting a 1277 on the SAT or a 28 on the ACT?

Correlation & Regression Correlation coefficient R describes the strength and direction of the relationship between two observed variables -1≤ r ≤ 1 -1 being a perfect negative correlation +1 being a perfect positive correlation 0 represents no relationship Linear Regression equations draw an imaginary line through the data cluster; the slope and intercept of the line is used to predict future values based on previous data Expressed as y = mx + b

Just because something has a strong correlation doesn’t mean there is a cause/effect relationship Many statistics can be misleading if you don’t pay close attention

Inferential statistics Determine whether a sample of data is due to chance or due to a meaningful trend Can compare two or more groups Can compare a group to a known norm Can compare longitudinal data from the same group

Statistical Significance Statistical Significance is achieved when the probability of getting a specific set of data by chance is extremely slim Typically, this probability is less than .05 or 5% IF the groups were the same, THEN the probability of getting that sample by chance would be very unlikely. Therefore, the researcher concludes that the groups are (probably) not the same. Because of the way statistics tests work, a researcher can never “prove” anything. They can only demonstrate how probable or improbable a certain event is. If someone claims they have “proven” anything, don’t trust them.

Type I Error: False Positive The researcher incorrectly determines that there is an effect when in reality none exists Type II Error: False Negative The researcher incorrectly determines that there is not effect when in reality there is an effect

While researchers want to find statistically significant differences between their groups, just because something is “statistically significant” doesn’t mean that it is practically significant. Studies with very large sample sizes can detect tiny differences that may not be meaningful

Be a thoughtful consumer of science Ask: How big was the sample? What was the effect size? Was the sample representative of the population? Did they use random assignment? Did they have an appropriate control group? Were the measures reliable and valid? Was the study blind/double-blind?

Psychological Research Methods

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